A theoretical study of Lifshitz transition for 2H-TaS2.

Lifshitz transition was proposed to explain a change of the topology structure in a Fermi surface induced by continuous lattice deformation without symmetry breaking since 1960. It is well known that the anomalies of the kinetic coefficients (the coefficient of heat conduction and electrical conductivity, viscosity, sound absorption, etc.) are usually closely connected with the Lifshitz transition behavior. 2H-TaS2 is a typical representative to study its anomalies of temperature dependence of heat capacity, resistivity, Hall effect, and magnetic susceptibility. Its geometrical structure of the charge density wave (CDW) phase and layer number dependence of carrier-sign alternation upon cooling in the Hall measurements have not been well understood. The geometrical structure (T-Ts) of the CDW phase was predicted through first principles calculations for bulk and mono-layer 2H-TaS2. Driven by electron-lattice coupling, Ta atoms contract to form a partially gapped CDW phase. The CDW phase has a larger average interlayer separation of S-S atoms in the adjacent two layers compared with the metal phase, which results in a weaker chemical bonding among S-S atoms in the adjacent two layers and then a narrower bandwidth of the energy band. The narrower bandwidth of the energy band leads to a larger density of states (DOS) in the out-of-plane direction above the Fermi level for the CDW phase. As the Fermi level continually drops from the DOS region with a negative slope to that with a positive slope on cooling, the reversal of the p → n type carrier and the pocket-vanishing-type Lifshitz transition occur in the bulk 2H-TaS2. However, the Fermi level slightly drops by 6 meV and happens to be at the positions of pseudo band gaps, so the reduction of in-plane DOS and total DOS is responsible for the always p-type carrier in the mono-layer samples. Our CDW vector of the k-space separation between two saddle points is QSP ≈ 0.62 GK and can provide a theoretical support for the "saddle-point" CDW mechanism proposed by Rice and Scott. Our theoretical explanation gives a new understanding of both Lifshitz transition for symmetry breaking and reversal for the p-n carrier sign in the Hall measurements in various two-dimensional transition metal disulfides.

Theoretical study of CDW phases for bulk NbX2 (X = S and Se).

In most two-dimensional transition metal chalcogenides, the superconducting phase coexists with the charge density wave (CDW) phase. There exists at least one case, i.e. bulk 2H-NbS2, that does not conform to this picture. Scientists have shown great interest in trying to experimentally find the CDW phase of bulk NbS2 since 1975. Is there any theoretically more stable thermodynamic state than its higher-temperature metal phase, especially in the case of charge injection? Theoretically more stable CDW bulk configurations (TC for 2H-NbS2 and TTs for 2H-NbSe2) with partial pseudo energy gaps were predicted through the harmonic phonon softening theory and first-principles calculations. The ratios of larger to smaller pseudo gaps around K-H segment in the Brillouin zone for CDW phases are basically equal to those of superconductivity phases for bulk 2H-NbX2 (X = S and Se). The CDW phase should coexist with its superconductor state below the critical temperature rather than the metal phase for bulk 2H-NbS2. The presence of CDW phase should be more easily observed experimentally when the injected charge reaches 0.5e/Nb18S36 for bulk 2H-NbS2. Our calculations of density of state (DOS) indicated that, during Nb atoms contracting to form the CDW phases with symmetry breaking in the in-plane direction, dominant conductive carriers are always of p-type for bulk 2H-NbS2 while the alternation of carrier type from p-type to n-type occurs for bulk 2H-NbSe2. The Fermi level continuously drops and then the M-L segment of the out-of-plane energy band emerges from the Fermi surface, which corresponds to the reversal of p-n type sign. Lifshitz transition of pocket-vanishing types occurs in the out-of-plane direction without symmetry breaking during the geometrical structural phase transition for bulk 2H-NbSe2. Our calculations have theoretically addressed the long-standing coexistence issue of CDW and superconducting phases.

Characteristics and fractionations of sediment oxygen demand in a complex tidal river network area.

Tidal river networks are affected by the tide and influenced by complex factors related to sediment oxygen demand (SOD). In this study, we used chemical inhibition to measure the oxygen consumption of different types of SOD to explore the specific oxygen consumption mechanism of sediments. Then, we evaluated the diffusion fluxes of the sediment-water interface and factors affecting SOD using diffusive gradients in thin films. Total SOD in the tidal river network area of the Pearl River basin was ∼0.5928 g/m2/day, which was 8.47% higher than that in the non-tidal river network area but lower than that in black and odorous water reported previously. In the tidal river network area, biological SOD was 15.6% higher in summer than in winter, and the difference in total SOD was greatly influenced by human activity. We observed a significant effect of sediment on SOD in winter, whereas there were no significant correlations between sediment and SOD in summer. Different particle-size distributions lead to different organic matter contents, resulting in different biological SOD ratios between seasons. Our study found that seasonal tidal changes can affect ion exchange at the sediment water interface, leading to changes in SOD.These findings will be of great significance for the study of phenomena associated with low dissolved oxygen in tidal river networks and provide directions for future sediment pollution control.

Assessment of the quality, diagnosis, and therapeutic recommendations of clinical practice guidelines on patients with Helicobacter pylori infection: A systematic review.

We conducted this study to systematically review and assess the current clinical practice guidelines (CPGs) related to the diagnosis and treatment of Helicobacter pylori (H. pylori) infection. The aim was to evaluate the quality of these included CPGs and provide clinicians with a convenient and comprehensive reference for updating their own CPGs. We searched four databases to identify eligible CPGs focusing on H. pylori diagnosis and treatment recommendations. The results were presented using evidence mappings. Quality and clinical applicability were assessed comprehensively using AGREE-II and AGREE-REX. Statistical tests, specifically Bonferroni tests, were employed to compare the quality between evidence-based guidelines and consensus. A total of 30 eligible CPGs were included, comprising 17 consensuses and 13 guidelines. The quality showed no statistical significance between consensuses and guidelines, mainly within the moderate to low range. Notably, recommendations across CPGs exhibited inconsistency. Nevertheless, concerning diagnosis, the urea breath test emerged as the most frequently recommended method for testing H. pylori. Regarding treatment, bismuth quadruple therapy stood out as the predominantly recommended eradication strategy, with high-dose dual therapy being a newly recommended option. Our findings suggest the need for specific organizations to update their CPGs on H. pylori or refer to recently published CPGs. Specifically, CPGs for pediatric cases require improvement and updating, while a notable absence of CPGs for the elderly was observed. Furthermore, there is a pressing need to improve the overall quality of CPGs related to H. pylori. Regarding recommendations, additional evidence is essential to elucidate the relationship between H. pylori infection and other diseases and refine test indications. Clinicians are encouraged to consider bismuth quadruple or high-dose dual therapy, incorporating locally sensitive antibiotics, as empirical radical therapy. .

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Recombinant human growth hormone (rhGH) is an effective therapeutic drug for improving short stature. Currently, rhGH can be used for various causes of short stature, including growth hormone deficiency, and the expansion of its clinical application has raised concerns about its safety. Based on existing evidence, when rhGH is used in a standardized manner for physiological replacement therapy, its safety profile is favorable. In clinical practice, attention should be focused on short-term safety during rhGH treatment, with the combination of literature evidence and clinical experience. There is still no definitive conclusion on the long-term safety due to insufficient duration of rhGH treatment. This paper reviews the possible adverse events that may occur during rhGH treatment and their risk control measures, aiming to help clinical physicians understand the overall safety of rhGH treatment and improve its clinical standardization.

Hyperuricemia and intravenous fat emulsion are risk factors for gout flares during active gastrointestinal bleeding: a case control study.

OBJECTIVE: It is well-established that patients with a history of gout are more susceptible to experiencing gastrointestinal bleeding. Gout flare during active gastrointestinal bleeding poses a significant challenge due to the gastrointestinal side effects of anti-inflammatory therapy. This study sought to investigate the risk factors associated with gout flares during episodes of gastrointestinal bleeding. METHODS: We conducted a retrospective observational study involving 94 patients who experienced active gastrointestinal bleeding and had a history of gout. This study was conducted at Jinhua Municipal Central Hospital from January 2019 to October 2022. We collected and recorded demographic information and clinical characteristics. RESULTS: Among the gout flare patients, hyperuricemia and intravenous fat emulsion therapy were more prevalent compared to those who remained stable (81.6% vs. 57.8% and 46.9% vs. 24.4%, p < 0.05). Multivariate logistic regression analysis revealed that both hyperuricemia (odds ratio 2.741, 95% CI 1.014-7.413, p = 0.047) and intravenous fat emulsion therapy (odds ratio 2.645, 95% CI 1.046-6.686, p = 0.040) were independent predictors of gout flares. Furthermore, gout attacks occurred sooner in patients receiving intravenous fat emulsion therapy compared to those not receiving it (median: 4 days (interquartile range: 2) vs. median: 5 days (interquartile range: 2.25), p = 0.049). CONCLUSION: Our study revealed a high incidence of gout flares during episodes of active gastrointestinal bleeding, with patients undergoing intravenous fat emulsion therapy and those with hyperuricemia being at increased risk.

MLMFNet: A multi-level modality fusion network for multi-modal accelerated MRI reconstruction.

Magnetic resonance imaging produces detailed anatomical and physiological images of the human body that can be used in the clinical diagnosis and treatment of diseases. However, MRI suffers its comparatively longer acquisition time than other imaging methods and is thus vulnerable to motion artifacts, which ultimately lead to likely failed or even wrong diagnosis. In order to perform faster reconstruction, deep learning-based methods along with traditional strategies such as parallel imaging and compressed sensing come into play in recent years in this field. Meanwhile, in order to better analyze the diseases, it is also often necessary to acquire images in the same region of interest under different modalities, which yield images with different contrast levels. However, most of these aforementioned methods tend to use single-modal images for reconstruction, neglecting the correlation and redundancy information embedded in MR images acquired with different modalities. While there are works on multi-modal reconstruction, the information is yet to be efficiently explored. In this paper, we propose an end-to-end neural network called MLMFNet, which helps the reconstruction of the target modality by using information from the auxiliary modality across feature channels and layers. Specifically, this is highlighted by three components: (I) An encoder based on UNet with a single-stream strategy that fuses auxiliary and target modalities; (II) a decoder that tends to multi-level features from all layers of the encoder, and (III) a channel attention module. Quantitative and qualitative analyses are performed on a public brain dataset and knee brain dataset, which show that the proposed method achieves satisfying results in MRI reconstruction within the multi-modal context, and also demonstrate its effectiveness and potential to be used in clinical practice.

Mutli-modal straight flow matching for accelerated MR imaging.

Diffusion models have garnered great interest lately in Magnetic Resonance (MR) image reconstruction. A key component of generating high-quality samples from noise is iterative denoising for thousands of steps. However, the complexity of inference steps has limited its applications. To solve the challenge in obtaining high-quality reconstructed images with fewer inference steps and computational complexity, we introduce a novel straight flow matching, based on a neural ordinary differential equation (ODE) generative model. Our model creates a linear path between undersampled images and reconstructed images, which can be accurately simulated with a few Euler steps. Furthermore, we propose a multi-modal straight flow matching model, which uses relatively easily available modalities as supplementary information to guide the reconstruction of target modalities. We introduce the low frequency fusion layer and the high frequency fusion layer into our multi-modal model, which has been proved to produce promising results in fusion tasks. The proposed multi-modal straight flow matching (MMSflow) achieves state-of-the-art performances in task of reconstruction in fastMRI and Brats-2020 and improves the sampling rate by an order of magnitude than other methods based on stochastic differential equations (SDE).

Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions.

The success of machine learning in predicting material properties is largely dependent on the design of the model. However, the current designs of deep learning models in materials science have the following prominent problems. First, the model design lacks a rational guidance strategy and heavily relies on a large amount of trial and error. Second, numerous deep learning models are utilized across various fields, each with its own advantages and disadvantages. Therefore, it is important to incorporate a fusion strategy to fully leverage them and further expand the design strategies of the models. To address these problems, we analyze that the main reason is the lack of a new feedback method rich in physical insights. In this study, we developed a feedback method called the Chemical Environment Clustering Vector (CECV) of compounds at different thresholds, which is rich in physical insights. Based on CECV, we rationally designed the Long Short-Term Memory and Gated Recurrent Unit fused with Deep Convolutional Neural Network (L-G-DCNN) to explore the field of structure-agnostic material property predictions. L-G-DCNN accurately captures the interactions between elements in compounds, enabling more accurate and efficient predictions of the material properties. Our results demonstrate that the performance of the L-G-DCNN surpasses the current state-of-the-art structure-agnostic models across 28 benchmark data sets, exhibiting superior sample efficiency and faster convergence speed. By employing different visualization methods, we demonstrate that the fusion strategy based on CECV significantly enhances the comprehension of the L-G-DCNN model design and provides a fresh perspective for researchers in the field of materials informatics.

Sequencing and Analysis of the Complete Mitochondrial Genome of Lentipes ikeae.

We sequenced and analyzed the complete mitochondrial genome of Lentipes ikeae and explored the phylogenetic relationships among Sicydiinae based on mitochondrial genome sequences. The complete mitochondrial genome sequence of L. ikeae was determined using the Illumina HiSeq X Ten sequencing platform, and the gene structural characteristics and base composition were analyzed. Based on the mitochondrial genome sequences of 28 Sicydiinae species published in GenBank and mitochondrial protein-coding genes (PCGs), Acanthogobius flavimanus (Gobionellinae) was selected as an outgroup to construct phylogenetic trees of Sicydiinae using the maximum likelihood and Bayesian inference methods. The mitochondrial genome of L. ikeae (GenBank number: OP764680) has a total length of 16,498 bp and encodes 13 PCGs, 22 transfer RNA genes, two ribosomal RNA genes, and a D-loop (control) region. Gene rearrangement is not observed. The mitochondrial genome of L. ikeae exhibits an AT preference, with AT skew > 0 and GC skew < 0 across the entire genome. The phylogenetic relationships of Sicydiinae based on 13 mitochondrial PCG sequences are Sicydium + (Stiphodon + (Sicyopus + Lentipes)) + Sicyopterus, indicating that Sicydium, Sicyopterus, Lentipes, and Stiphodon are all monophyletic groups.

Isolation, identification and whole-genome analysis of an Achromobacter strain with a novel sulfamethazine resistance gene and sulfamethazine degradation gene cluster.

A sulfamethazine (SM2) degrading strain, Achromobacter mucicolens JD417, was isolated from sulfonamide-contaminated sludge using gradient acclimation. Optimal SM2 degradation conditions were pH 7, 36 °C, and 5 % inoculum, achieving a theoretical maximum degradation rate of 48 % at 50 ppm SM2. Cell growth followed the Haldane equation across different SM2 concentrations. Whole-genome sequencing of the strain revealed novel functional annotations, including a sulfonamide resistance gene (sul4) encoding dihydropteroate synthase, two flavin-dependent monooxygenase genes (sadA and sadB) crucial for SM2 degradation, and unique genomic islands related to metabolism, pathogenicity, and resistance. Comparative genomics analysis showed good collinearity and homology with other Achromobacter species exhibiting organics resistance or degradation capabilities. This study reveals the novel molecular resistance and degradation mechanisms and genetic evolution of an SM2-degrading strain, providing insights into the bioremediation of sulfonamide-contaminated environments.

The profile of blood microbiome in new-onset type 1 diabetes children.

Blood microbiome signatures in patients with type 1 diabetes (T1D) remain unclear. We profile blood microbiome using 16S rRNA gene sequencing in 77 controls and 64 children with new-onset T1D, and compared it with the gut and oral microbiomes. The blood microbiome of patients with T1D is characterized by increased diversity and perturbed microbial features, with a significant increase in potentially pathogenic bacteria compared with controls. Thirty-six representative genera of blood microbiome were identified by random forest analysis, providing strong discriminatory power for T1D with an AUC of 0.82. PICRUSt analysis suggested that bacteria capable of inducing inflammation were more likely to enter the bloodstream in T1D. The overlap of the gut and oral microbiome with the blood microbiome implied potential translocation of bacteria from the gut and oral cavity to the bloodstream. Our study raised the necessity of further mechanistic investigations into the roles of blood microbiome in T1D.

Validation of an AI-Powered Automated X-ray Bone Age Analyzer in Chinese Children and Adolescents: A Comparison with the Tanner-Whitehouse 3 Method.

INTRODUCTION: Automated bone age assessment (BAA) is of growing interest because of its accuracy and time efficiency in daily practice. In this study, we validated the clinical applicability of a commercially available artificial intelligence (AI)-powered X-ray bone age analyzer equipped with a deep learning-based automated BAA system and compared its performance with that of the Tanner-Whitehouse 3 (TW-3) method. METHODS: Radiographs prospectively collected from 30 centers across various regions in China, including 900 Chinese children and adolescents, were assessed independently by six doctors (three experts and three residents) and an AI analyzer for TW3 radius, ulna, and short bones (RUS) and TW3 carpal bone age. The experts' mean estimates were accepted as the gold standard. The performance of the AI analyzer was compared with that of each resident. RESULTS: For the estimation of TW3-RUS, the AI analyzer had a mean absolute error (MAE) of 0.48 ± 0.42. The percentage of patients with an absolute error of < 1.0 years was 86.78%. The MAE was significantly lower than that of rater 1 (0.54 ± 0.49, P = 0.0068); however, it was not significant for rater 2 (0.48 ± 0.48) or rater 3 (0.49 ± 0.46). For TW3 carpal, the AI analyzer had an MAE of 0.48 ± 0.65. The percentage of patients with an absolute error of < 1.0 years was 88.78%. The MAE was significantly lower than that of rater 2 (0.58 ± 0.67, P = 0.0018) and numerically lower for rater 1 (0.54 ± 0.64) and rater 3 (0.50 ± 0.53). These results were consistent for the subgroups according to sex, and differences between the age groups were observed. CONCLUSION: In this comprehensive validation study conducted in China, an AI-powered X-ray bone age analyzer showed accuracies that matched or exceeded those of doctor raters. This method may improve the efficiency of clinical routines by reducing reading time without compromising accuracy.

Assessing bone age, or how developed a child's skeleton is, is important in medical care, but the standard method can be time-consuming. Using AI to automatically assess bone age from X-ray images may improve efficiency without reducing accuracy. In this study, we evaluated how well an AI-powered X-ray bone age analyzer performed compared to the established Tanner­Whitehouse 3 (TW-3) method. X-ray images from 900 Chinese children and adolescents were collected from 30 centers. Six doctors (three experts, three residents) and the AI system independently assessed the TW-3 radius, ulna, and short bones (RUS) and TW-3 carpal bone age. The experts' assessments were considered the gold standard. The AI analyzer had an average error of 0.48 years for TW3-RUS bone age, with 87% of assessments within 1 year of the experts. For TW3 carpal bone age, the AI had an average error of 0.48 years, with 89% within 1 year. These results were similar to or better than those of the resident raters. These findings show the AI-powered analyzer can assess bone age as accurately as human raters. This technology may improve clinical efficiency by reducing the time required for bone age assessments without compromising accuracy.

An international multidisciplinary consensus on pediatric metabolic dysfunction-associated fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent in children and adolescents, particularly those with obesity. NAFLD is considered a hepatic manifestation of the metabolic syndrome due to its close associations with abdominal obesity, insulin resistance, and atherogenic dyslipidemia. Experts have proposed an alternative terminology, metabolic dysfunction-associated fatty liver disease (MAFLD), to better reflect its pathophysiology. This study aimed to develop consensus statements and recommendations for pediatric MAFLD through collaboration among international experts. METHODS: A group of 65 experts from 35 countries and six continents, including pediatricians, hepatologists, and endocrinologists, participated in a consensus development process. The process encompassed various aspects of pediatric MAFLD, including epidemiology, mechanisms, screening, and management. FINDINGS: In round 1, we received 65 surveys from 35 countries and analyzed these results, which informed us that 73.3% of respondents agreed with 20 draft statements while 23.8% agreed somewhat. The mean percentage of agreement or somewhat agreement increased to 80.85% and 15.75%, respectively, in round 2. The final statements covered a wide range of topics related to epidemiology, pathophysiology, and strategies for screening and managing pediatric MAFLD. CONCLUSIONS: The consensus statements and recommendations developed by an international expert panel serve to optimize clinical outcomes and improve the quality of life for children and adolescents with MAFLD. These findings emphasize the need for standardized approaches in diagnosing and treating pediatric MAFLD. FUNDING: This work was funded by the National Natural Science Foundation of China (82070588, 82370577), the National Key R&D Program of China (2023YFA1800801), National High Level Hospital Clinical Research Funding (2022-PUMCH-C-014), the Wuxi Taihu Talent Plan (DJTD202106), and the Medical Key Discipline Program of Wuxi Health Commission (ZDXK2021007).

Treatment of glutaric aciduria type I (GA-I) via intracerebroventricular delivery of GCDH.

Glutaric aciduria type I (GA-I) is an autosomal recessive genetic disorder caused by a deficiency in glutaryl-CoA dehydrogenase (GCDH). Patients who do not receive proper treatment may die from acute encephalopathic crisis. Current treatments for GA-I include a low-lysine diet combined with oral supplementation of L-carnitine. A mouse model of Gcdh c.422_428del/c.422_428del (Gcdh -/-) was generated in our laboratory using CRISPR/Cas9. Gcdh -/- mice had significantly higher levels of glutaric acid (GA) in the plasma, liver, and brain than those in wild-type C57BL/6 mice. When given a high-protein diet (HPD) for two days, approximately 60% of Gcdh -/- mice did not survive the metabolic stress. To evaluate whether GCDH gene replacement therapy could be used to provide sustained treatment for patients with GA-1, we prepared a recombinant adeno-associated virus (rAAV) carrying a human GCDH expression cassette and injected it into Gcdh -/- neonates for a proof-of-concept (PoC) study. Our study demonstrated that delivering rAAV to the central nervous system (CNS), but not the peripheral system, significantly increased the survival rate under HPD exposure. Our study also demonstrated that rAAVPHP.eB mediated a higher efficiency than that of rAAV9 in increasing the survival rate. Surviving mice showed dose-dependent GCDH protein expression in the CNS and downregulation of GA levels. Our study demonstrated that AAV-based gene replacement therapy was effective for GA-I treatment and provided a feasible solution for this unmet medical need.